Governed AI for healthcare and life sciences
Sidegem builds AI-enabled software that is secure, explainable, and operational—designed for real workflows, regulated environments, and measurable outcomes.
Governed AI means audit trails, human-in-the-loop controls, and clear system behavior you can defend in regulated settings.
Security-aware engineering • Traceability • Operational readiness • Production delivery
AI systems that hold up in the real world
Sidegem delivers end-to-end solutions—from data ingestion and workflow automation to production web applications—built with governance and reliability in mind.
Clinical document intelligence
Convert unstructured healthcare documents into structured, actionable data with prioritization and change detection.
AI assistants and knowledge agents
Domain-grounded assistants that ask clarifying questions, guide users to next steps, and improve discovery and decision-making.
Production software delivery
Reliable platforms and web applications delivered with disciplined engineering practices and operational visibility.
Services
AI workflow automation
(healthcare-grade)
Pipeline design and implementation for document processing, triage, summarization, and downstream integrations.
AI assistants and agentic experiences
Conversational systems grounded in approved knowledge sources, with guardrails and measurable goals.
Full-stack delivery
End-to-end implementation across backend, frontend, and data infrastructure—built to deploy and maintain.
Engagement models: Advisory sprint • Build & deliver • Ongoing retainer
Who we work with
Teams in healthcare and life sciences that need reliable AI in production, not prototypes. Typical partners include clinical operations, medical affairs, and data teams responsible for regulated workflows.
Regulated workflows
You need traceability, auditability, and clear exception handling.
Operational accountability
Success is measured by time saved, reduced risk, and dependable delivery.
High-stakes decisions
Human review and safety controls are part of the system design.
Delivery approach
Define
the workflow, constraints, and success metrics
Design
the architecture, data flows, and governance/controls
Build
in small increments with testing and operational readiness
Deploy & iterate
with monitoring, feedback loops, and continuous improvements
Case studies
Structured data extraction & clinical triage
An automated document-processing pipeline that converts unstructured healthcare documents into actionable data.
Operational impact:
- •AI-driven clinical triage: Identifies urgent vs. routine documents based on medical meaning and flags critical items for review.
- •Medication change detection: Detects new, discontinued, or changed prescriptions by comparing medication information across documents.
- •Chart-ready summaries: Produces concise clinical summaries highlighting what changed, what needs action, and why.
- •Faster review cycles: Reduces manual sorting and improves turnaround for priority cases.
Medical foundation knowledge assistant
A chatbot grounded in a medical foundation's website knowledge base, using an agentic conversation flow.
Supported outcomes:
- •Increased discovery through clarifying questions and guided exploration
- •Improved preparation for patient meetings
- •Surfaced relevant drugs and therapies for clinicians
- •Encouraged practice-improving behavior via structured, context-aware guidance
Technical capabilities
We design secure data pipelines, evaluation harnesses, and governance controls that make AI reliable in production. Tooling is chosen to fit the workflow, data sensitivity, and operational environment.
Common tools include AWS, Python, React, Postgres, vector databases (e.g., Pinecone), and modern model/provider ecosystems (e.g., OpenAI and Gemini).
Discuss your workflow and constraints
Schedule a short call to outline the use case, data inputs, and requirements.